Conference Paper/Proceeding/Abstract 571 views 1369 downloads
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), Pages: 1 - 6
Swansea University Author:
Cinzia Giannetti
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DOI (Published version): 10.1109/INISTA.2019.8778417
Abstract
This paper proposes a deep learning framework where wavelet transforms (WT), 2-dimensional Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) stacked autoencoders (SAE) are combined towards single-step time series prediction. Within the framework, the input dataset is denoised us...
| Published in: | 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) |
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| ISBN: | 978-1-7281-1863-5 978-1-7281-1862-8 |
| Published: |
Sofia, Bulgaria
IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)
2019
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa51622 |
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| Abstract: |
This paper proposes a deep learning framework where wavelet transforms (WT), 2-dimensional Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) stacked autoencoders (SAE) are combined towards single-step time series prediction. Within the framework, the input dataset is denoised using wavelet decomposition, before learning in an unsupervised manner using SAEs comprising bidirectional Convolutional LSTM (ConvLSTM) layers to predict a single-step ahead value. To evaluate our proposed framework, we compared its performance to two (2) state-of-the-art deep learning predictive models using three open-source univariate time series datasets. The experimental results support the value of the approach when applied to univariate time series prediction. |
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| College: |
College of Engineering |
| Start Page: |
1 |
| End Page: |
6 |